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Cynthia Rudin
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- affiliation: Duke University, USA
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2020 – today
- 2024
- [j51]Manickam Ashokkumar
, Wenwen Mei
, Jackson J. Peterson
, Yuriko Harigaya
, David M. Murdoch
, David M. Margolis
, Caleb Kornfein
, Alexander X. Oesterling
, Zhicheng Guo
, Cynthia Rudin
, Yuchao Jiang
, Edward P. Browne
:
Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genom. Proteom. Bioinform. 22(1) (2024) - [j50]Cheng Ding
, Zhicheng Guo
, Cynthia Rudin
, Ran Xiao
, Amit J. Shah
, Duc H. Do
, Randall J. Lee, Gari D. Clifford
, Fadi B. Nahab
, Xiao Hu
:
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. IEEE J. Biomed. Health Informatics 28(5): 2650-2661 (2024) - [c85]Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference. AAAI 2024: 22331-22340 - [c84]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Survival Trees. AISTATS 2024: 352-360 - [c83]Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin:
Sparse and Faithful Explanations Without Sparse Models. AISTATS 2024: 2071-2079 - [c82]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Safe and Interpretable Estimation of Optimal Treatment Regimes. AISTATS 2024: 2134-2142 - [c81]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. AISTATS 2024: 3340-3348 - [c80]Stephen Hahn, Jerry Yin, Rico Zhu, Weihan Xu, Yue Jiang, Simon Mak, Cynthia Rudin:
SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. KDD 2024: 5050-5060 - [i106]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Survival Trees. CoRR abs/2401.15330 (2024) - [i105]Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin:
Sparse and Faithful Explanations Without Sparse Models. CoRR abs/2402.09702 (2024) - [i104]Varun Babbar, Zhicheng Guo, Cynthia Rudin:
What is different between these datasets? CoRR abs/2403.05652 (2024) - [i103]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR abs/2404.04714 (2024) - [i102]Cheng Ding, Zhicheng Guo, Zhaoliang Chen, Randall J. Lee, Cynthia Rudin, Xiao Hu:
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals. CoRR abs/2404.17667 (2024) - [i101]Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin:
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. CoRR abs/2406.06386 (2024) - [i100]Frank Willard, Luke Moffett, Emmanuel Mokel, Jon Donnelly, Stark Guo, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin:
This Looks Better than That: Better Interpretable Models with ProtoPNeXt. CoRR abs/2406.14675 (2024) - [i99]Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Amazing Things Come From Having Many Good Models. CoRR abs/2407.04846 (2024) - 2023
- [j49]Lina Zhou, Cynthia Rudin, Matthew C. Gombolay, Jim Spohrer, Michelle Zhou, Souren Paul:
From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. AIS Trans. Hum. Comput. Interact. 15(1): 111-135 (2023) - [j48]Cynthia Rudin, Yaron Shaposhnik:
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. J. Mach. Learn. Res. 24: 16:1-16:44 (2023) - [c79]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Regression Trees. AAAI 2023: 11270-11279 - [c78]Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin:
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. ACL (2) 2023: 1627-1638 - [c77]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - [c76]Stephen Hahn
, Rico Zhu
, Simon Mak
, Cynthia Rudin
, Yue Jiang
:
An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. KDD 2023: 4089-4099 - [c75]Yanchen Jessie Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz
, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin:
A user interface to communicate interpretable AI decisions to radiologists. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2023 - [c74]Yanchen Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin:
A user interface to communicate interpretable AI decisions to radiologists (Erratum). Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2023 - [c73]Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin:
OKRidge: Scalable Optimal k-Sparse Ridge Regression. NeurIPS 2023 - [c72]Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne:
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. NeurIPS 2023 - [c71]Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin:
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. NeurIPS 2023 - [c70]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. NeurIPS 2023 - [c69]Chudi Zhong, Zhi Chen, Jiachang Liu, Margo I. Seltzer, Cynthia Rudin:
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models. NeurIPS 2023 - [c68]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
Variable importance matching for causal inference. UAI 2023: 1174-1184 - [i98]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR abs/2302.11715 (2023) - [i97]Zhi Chen, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. CoRR abs/2303.16047 (2023) - [i96]Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin:
OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. CoRR abs/2304.06686 (2023) - [i95]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - [i94]Marco Morucci, Vittorio Orlandi, Harsh Parikh, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
A Double Machine Learning Approach to Combining Experimental and Observational Data. CoRR abs/2307.01449 (2023) - [i93]Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu:
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. CoRR abs/2307.05339 (2023) - [i92]Sully F. Chen
, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin:
Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. CoRR abs/2307.05385 (2023) - [i91]Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne:
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. CoRR abs/2309.13775 (2023) - [i90]Zhicheng Guo, Cheng Ding, Duc H. Do, Amit J. Shah, Randall J. Lee, Xiao Hu, Cynthia Rudin:
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection. CoRR abs/2310.09203 (2023) - [i89]Han Zhang, Rayehe Karimi Mahabadi, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson:
Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties. CoRR abs/2310.12869 (2023) - [i88]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Estimating Trustworthy and Safe Optimal Treatment Regimes. CoRR abs/2310.15333 (2023) - [i87]Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin:
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. CoRR abs/2310.18589 (2023) - [i86]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. CoRR abs/2310.19726 (2023) - [i85]Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin:
Fast and Interpretable Mortality Risk Scores for Critical Care Patients. CoRR abs/2311.13015 (2023) - [i84]Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B. Nahab, Xiao Hu:
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables. CoRR abs/2312.02300 (2023) - [i83]Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, M. Brandon Westover:
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges. CoRR abs/2312.10056 (2023) - [i82]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. CoRR abs/2312.10569 (2023) - 2022
- [j47]Chaofan Chen
, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang
:
A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decis. Support Syst. 152: 113647 (2022) - [j46]Tong Wang
, Cynthia Rudin
:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. INFORMS J. Comput. 34(3): 1626-1643 (2022) - [j45]Chunxiao Li, Cynthia Rudin, Tyler H. McCormick:
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. J. Mach. Learn. Res. 23: 96:1-96:55 (2022) - [j44]Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
MALTS: Matching After Learning to Stretch. J. Mach. Learn. Res. 23: 240:1-240:42 (2022) - [c67]Hayden McTavish
, Chudi Zhong, Reto Achermann, Ilias Karimalis
, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. AAAI 2022: 9604-9613 - [c66]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. AISTATS 2022: 9304-9333 - [c65]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Mango I. Seltzer:
Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. CIKM Workshops 2022 - [c64]Lesia Semenova, Cynthia Rudin, Ronald Parr:
On the Existence of Simpler Machine Learning Models. FAccT 2022: 1827-1858 - [c63]Alina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Regina Schwartz
, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin:
Interpretable deep learning models for better clinician-AI communication in clinical mammography. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2022 - [c62]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. NeurIPS 2022 - [c61]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. NeurIPS 2022 - [c60]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data poisoning attacks on off-policy policy evaluation methods. UAI 2022: 1264-1274 - [c59]Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer:
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. IEEE VIS (Short Papers) 2022: 60-64 - [i81]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. CoRR abs/2202.11389 (2022) - [i80]Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun
, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover:
Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. CoRR abs/2203.04920 (2022) - [i79]Haiyang Huang, Zhi Chen, Cynthia Rudin:
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. CoRR abs/2204.10926 (2022) - [i78]Yishay Mansour, Michal Moshkovitz, Cynthia Rudin:
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. CoRR abs/2206.04266 (2022) - [i77]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. CoRR abs/2209.08040 (2022) - [i76]Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer:
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. CoRR abs/2209.09227 (2022) - [i75]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. CoRR abs/2210.05846 (2022) - [i74]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo I. Seltzer:
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. CoRR abs/2210.06825 (2022) - [i73]Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover:
Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. CoRR abs/2211.05207 (2022) - [i72]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Regression Trees. CoRR abs/2211.14980 (2022) - 2021
- [j43]Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. J. Artif. Intell. Res. 71: 319-346 (2021) - [j42]Stefano Tracà, Cynthia Rudin, Weiyu Yan:
Regulating Greed Over Time in Multi-Armed Bandits. J. Mach. Learn. Res. 22: 3:1-3:99 (2021) - [j41]Tianyu Wang
, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. J. Mach. Learn. Res. 22: 31:1-31:41 (2021) - [j40]Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. J. Mach. Learn. Res. 22: 201:1-201:73 (2021) - [j39]Beau Coker
, Cynthia Rudin
, Gary King
:
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Manag. Sci. 67(10): 6174-6197 (2021) - [j38]Alina Jade Barnett
, Fides Regina Schwartz
, Chaofan Tao, Chaofan Chen
, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin
:
A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat. Mach. Intell. 3(12): 1061-1070 (2021) - [j37]Jianyou Wang, Cynthia Rudin, Yuren Zhou, Christopher Suh, Xiaoxuan Zhang:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. Trans. Assoc. Comput. Linguistics 9: 605-620 (2021) - [c58]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. AIES 2021: 316-326 - [d1]Alexander C. Ogren, Zhi Chen, L. Catherine Brinson, Mary Bastawrous, Cynthia Rudin, Chiara Daraio:
2D elastodynamic metamaterials. UCI Machine Learning Repository, 2021 - [i71]Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. CoRR abs/2101.01867 (2021) - [i70]Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. CoRR abs/2103.03775 (2021) - [i69]Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong:
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. CoRR abs/2103.11251 (2021) - [i68]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. CoRR abs/2103.12308 (2021) - [i67]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. CoRR abs/2105.00060 (2021) - [i66]Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. CoRR abs/2105.05885 (2021) - [i65]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations. CoRR abs/2106.02605 (2021) - [i64]Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin:
Multitask Learning for Citation Purpose Classification. CoRR abs/2106.13275 (2021) - [i63]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. CoRR abs/2107.05605 (2021) - [i62]Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin:
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales. CoRR abs/2109.07623 (2021) - [i61]Zhi Chen, Alexander C. Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin:
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. CoRR abs/2111.05949 (2021) - [i60]Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. CoRR abs/2112.00798 (2021) - 2020
- [j36]Alexander S. Rich, Cynthia Rudin, David M. P. Jacoby, Robin Freeman, Oliver R. Wearn, Henry Shevlin, Kanta Dihal, Seán S. ÓhÉigeartaigh, James Butcher, Marco Lippi, Przemyslaw Palka, Paolo Torroni
, Shannon Wongvibulsin, Edmon Begoli, Gisbert Schneider, Stephen Cave, Mona Sloane, Emanuel Moss, Iyad Rahwan, Ken Goldberg, David Howard, Luciano Floridi, Jack Stilgoe:
AI reflections in 2019. Nat. Mach. Intell. 2(1): 2-9 (2020) - [j35]Zhi Chen
, Yijie Bei
, Cynthia Rudin
:
Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12): 772-782 (2020) - [j34]Jiayun Dong
, Cynthia Rudin
:
Exploring the cloud of variable importance for the set of all good models. Nat. Mach. Intell. 2(12): 810-824 (2020) - [c57]Jerry Liu, Nathan O'Hara, Alexander Rubin, Rachel Lea Draelos
, Cynthia Rudin:
Metaphor Detection Using Contextual Word Embeddings From Transformers. Fig-Lang@ACL 2020: 250-255 - [c56]Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Lea Draelos
, Cynthia Rudin:
A Transformer Approach to Contextual Sarcasm Detection in Twitter. Fig-Lang@ACL 2020: 270-275 - [c55]M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. AISTATS 2020: 3252-3262 - [c54]Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin:
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CVPR 2020: 2434-2442 - [c53]Tianyu Wang
, Weicheng Ye, Dawei Geng, Cynthia Rudin:
Towards Practical Lipschitz Bandits. FODS 2020: 129-138 - [c52]Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer:
Generalized and Scalable Optimal Sparse Decision Trees. ICML 2020: 6150-6160 - [c51]Tianyu Wang, Cynthia Rudin:
Bandits for BMO Functions. ICML 2020: 9996-10006 - [c50]Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. UAI 2020: 1089-1098 - [i59]Zhi Chen, Yijie Bei, Cynthia Rudin:
Concept Whitening for Interpretable Image Recognition. CoRR abs/2002.01650 (2020) - [i58]M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. CoRR abs/2003.00964 (2020) - [i57]Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. CoRR abs/2003.01805 (2020) - [i56]Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin:
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CoRR abs/2003.03808 (2020) - [i55]Caroline Wang, Bin Han, Bhrij Patel, Feroze Mohideen, Cynthia Rudin:
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. CoRR abs/2005.04176 (2020) - [i54]Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer:
Generalized Optimal Sparse Decision Trees. CoRR abs/2006.08690 (2020) - [i53]Tianyu Wang, Cynthia Rudin:
Bandits for BMO Functions. CoRR abs/2007.08703 (2020) - [i52]Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi:
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning. CoRR abs/2011.11020 (2020) - [i51]Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization. CoRR abs/2012.04456 (2020)
2010 – 2019
- 2019
- [j33]Gah-Yi Ban
, Cynthia Rudin
:
The Big Data Newsvendor: Practical Insights from Machine Learning. Oper. Res. 67(1): 90-108 (2019) - [j32]Berk Ustun
, Cynthia Rudin:
Learning Optimized Risk Scores. J. Mach. Learn. Res. 20: 150:1-150:75 (2019) - [j31]Aaron Fisher, Cynthia Rudin, Francesca Dominici:
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. J. Mach. Learn. Res. 20: 177:1-177:81 (2019) - [j30]Cynthia Rudin
:
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5): 206-215 (2019) - [c49]Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost-Exact Matching for Causal Inference. AISTATS 2019: 2445-2453 - [c48]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. HCOMP 2019: 32-40 - [c47]Cynthia Rudin:
Do Simpler Models Exist and How Can We Find Them? KDD 2019: 1-2 - [c46]Xiyang Hu, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. NeurIPS 2019: 7265-7273 - [c45]Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su:
This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS 2019: 8928-8939 - [c44]Stefano Tracà, Weiyu Yan, Cynthia Rudin:
Reducing Exploration of Dying Arms in Mortal Bandits. UAI 2019: 156-163 - [c43]M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost Matching Exactly With Instrumental Variables. UAI 2019: 1116-1126 - [i50]Jiayun Dong, Cynthia Rudin:
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models. CoRR abs/1901.03209 (2019) - [i49]Tianyu Wang, Dawei Geng, Cynthia Rudin:
A Practical Bandit Method with Advantages in Neural Network Tuning. CoRR abs/1901.09277 (2019) - [i48]Xiyang Hu
, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. CoRR abs/1904.12847 (2019) - [i47]Cynthia Rudin, David Carlson:
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis. CoRR abs/1906.01998 (2019) - [i46]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. CoRR abs/1906.10651 (2019) - [i45]M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost-Matching-Exactly With Instrumental Variables. CoRR abs/1906.11658 (2019) - [i44]Stefano Tracà, Cynthia Rudin, Weiyu Yan:
Reducing Exploration of Dying Arms in Mortal Bandits. CoRR abs/1907.02571 (2019) - [i43]Lesia Semenova, Cynthia Rudin:
A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning. CoRR abs/1908.01755 (2019) - 2018
- [j29]Cynthia Rudin
, Berk Ustun:
Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. Interfaces 48(5): 449-466 (2018) - [j28]Cynthia Rudin, Seyda Ertekin:
Learning customized and optimized lists of rules with mathematical programming. Math. Program. Comput. 10(4): 659-702 (2018) - [c42]Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin:
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions. AAAI 2018: 3530-3537 - [c41]Chaofan Chen, Cynthia Rudin:
An Optimization Approach to Learning Falling Rule Lists. AISTATS 2018: 604-612 - [c40]Cynthia Rudin, Yining Wang:
Direct Learning to Rank And Rerank. AISTATS 2018: 775-783 - [c39]Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin:
New Techniques for Preserving Global Structure and Denoising With Low Information Loss in Single-Image Super-Resolution. CVPR Workshops 2018: 874-881 - [i42]Cynthia Rudin, Yining Wang:
Direct Learning to Rank and Rerank. CoRR abs/1802.07400 (2018) - [i41]Siong Thye Goh, Cynthia Rudin:
A Minimax Surrogate Loss Approach to Conditional Difference Estimation. CoRR abs/1803.03769 (2018) - [i40]Beau Coker, Cynthia Rudin, Gary King:
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. CoRR abs/1804.08646 (2018) - [i39]Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin:
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution. CoRR abs/1805.03383 (2018) - [i38]Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference. CoRR abs/1806.06802 (2018) - [i37]Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, Cynthia Rudin:
This looks like that: deep learning for interpretable image recognition. CoRR abs/1806.10574 (2018) - [i36]Ramin Moghaddass, Cynthia Rudin:
Bayesian Patchworks: An Approach to Case-Based Reasoning. CoRR abs/1809.03541 (2018) - [i35]Cynthia Rudin, Caroline Wang, Beau Coker:
The age of secrecy and unfairness in recidivism prediction. CoRR abs/1811.00731 (2018) - [i34]John Benhart, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin:
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. CoRR abs/1811.05067 (2018) - [i33]Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
MALTS: Matching After Learning to Stretch. CoRR abs/1811.07415 (2018) - [i32]Cynthia Rudin:
Please Stop Explaining Black Box Models for High Stakes Decisions. CoRR abs/1811.10154 (2018) - [i31]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
An Interpretable Model with Globally Consistent Explanations for Credit Risk. CoRR abs/1811.12615 (2018) - 2017
- [j27]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
A Bayesian Framework for Learning Rule Sets for Interpretable Classification. J. Mach. Learn. Res. 18: 70:1-70:37 (2017) - [j26]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. J. Mach. Learn. Res. 18: 234:1-234:78 (2017) - [c38]Himabindu Lakkaraju, Cynthia Rudin:
Learning Cost-Effective and Interpretable Treatment Regimes. AISTATS 2017: 166-175 - [c37]Hongyu Yang, Cynthia Rudin, Margo I. Seltzer:
Scalable Bayesian Rule Lists. ICML 2017: 3921-3930 - [c36]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists. KDD 2017: 35-44 - [c35]Berk Ustun
, Cynthia Rudin:
Optimized Risk Scores. KDD 2017: 1125-1134 - [i30]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. CoRR abs/1704.01701 (2017) - [i29]Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, Tianyu Wang:
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. CoRR abs/1707.06315 (2017) - [i28]Chaofan Chen, Cynthia Rudin:
An optimization approach to learning falling rule lists. CoRR abs/1710.02572 (2017) - [i27]Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin:
Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions. CoRR abs/1710.04806 (2017) - [i26]Tong Wang, Cynthia Rudin:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. CoRR abs/1710.05426 (2017) - [i25]Fulton Wang, Cynthia Rudin:
Dimension Reduction for Robust Covariate Shift Correction. CoRR abs/1711.10938 (2017) - 2016
- [j25]Ramin Moghaddass, Cynthia Rudin, David Madigan:
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. J. Mach. Learn. Res. 17: 185:1-185:24 (2016) - [j24]Berk Ustun
, Cynthia Rudin:
Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102(3): 349-391 (2016) - [j23]William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price
, Melissa Lamar, Dana L. Penney:
Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test. Mach. Learn. 102(3): 393-441 (2016) - [c34]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS 2016: 305-313 - [c33]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
Bayesian Rule Sets for Interpretable Classification. ICDM 2016: 1269-1274 - [c32]Benjamin Letham, Lydia M. Letham, Cynthia Rudin:
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. KDD 2016: 1695-1704 - [i24]Hongyu Yang, Cynthia Rudin, Margo I. Seltzer:
Scalable Bayesian Rule Lists. CoRR abs/1602.08610 (2016) - [i23]William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, Dana L. Penney:
Interpretable Machine Learning Models for the Digital Clock Drawing Test. CoRR abs/1606.07163 (2016) - [i22]Himabindu Lakkaraju, Cynthia Rudin:
Learning Cost-Effective Treatment Regimes using Markov Decision Processes. CoRR abs/1610.06972 (2016) - 2015
- [j22]Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri:
Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data 3(1): 3-21 (2015) - [j21]Seyda Ertekin, Cynthia Rudin:
A Bayesian Approach to Learning Scoring Systems. Big Data 3(4): 267-276 (2015) - [j20]Theja Tulabandhula, Cynthia Rudin:
Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. Mach. Learn. 100(2-3): 183-216 (2015) - [c31]Fulton Wang, Cynthia Rudin:
Falling Rule Lists. AISTATS 2015 - [i21]Berk Ustun, Cynthia Rudin:
Supersparse Linear Integer Models for Optimized Medical Scoring Systems. CoRR abs/1502.04269 (2015) - [i20]Been Kim, Cynthia Rudin, Julie A. Shah:
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. CoRR abs/1503.01161 (2015) - [i19]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. CoRR abs/1504.07614 (2015) - [i18]Stefano Tracà, Cynthia Rudin:
Regulating Greed Over Time. CoRR abs/1505.05629 (2015) - [i17]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. CoRR abs/1506.07609 (2015) - [i16]Fulton Wang, Cynthia Rudin:
Causal Falling Rule Lists. CoRR abs/1510.05189 (2015) - [i15]Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan:
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. CoRR abs/1511.01644 (2015) - [i14]Tong Wang, Cynthia Rudin:
Learning Optimized Or's of And's. CoRR abs/1511.02210 (2015) - 2014
- [j19]Theja Tulabandhula, Cynthia Rudin:
Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing. Big Data 2(2): 97-112 (2014) - [j18]Been Kim, Cynthia Rudin:
Learning about meetings. Data Min. Knowl. Discov. 28(5-6): 1134-1157 (2014) - [j17]Seyda Ertekin, Cynthia Rudin, Haym Hirsh:
Approximating the crowd. Data Min. Knowl. Discov. 28(5-6): 1189-1221 (2014) - [j16]Cynthia Rudin, Seyda Ertekin, Rebecca J. Passonneau, Axinia Radeva, Ashish Tomar, Boyi Xie, Stanley Lewis, Mark Riddle, Debbie Pangsrivinij, Tyler H. McCormick
:
Analytics for Power Grid Distribution Reliability in New York City. Interfaces 44(4): 364-383 (2014) - [j15]Cynthia Rudin, Kiri L. Wagstaff
:
Machine learning for science and society. Mach. Learn. 95(1): 1-9 (2014) - [j14]Theja Tulabandhula, Cynthia Rudin:
On combining machine learning with decision making. Mach. Learn. 97(1-2): 33-64 (2014) - [c30]Dingquan Wang, Rebecca J. Passonneau, Michael Collins, Cynthia Rudin:
Modeling Weather Impact on a Secondary Electrical Grid. ANT/SEIT 2014: 631-638 - [c29]Jonathan H. Huggins, Cynthia Rudin:
Toward a Theory of Pattern Discovery. ISAIM 2014 - [c28]Theja Tulabandhula, Cynthia Rudin:
Generalization Bounds for Learning with Linear and Quadratic Side Knowledge. ISAIM 2014 - [c27]Theja Tulabandhula, Cynthia Rudin:
Robust Optimization using Machine Learning for Uncertainty Sets. ISAIM 2014 - [c26]Siong Thye Goh
, Cynthia Rudin:
Box drawings for learning with imbalanced data. KDD 2014: 333-342 - [c25]Cynthia Rudin:
Algorithms for interpretable machine learning. KDD 2014: 1519 - [c24]Been Kim, Cynthia Rudin, Julie A. Shah:
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS 2014: 1952-1960 - [c23]Jonathan H. Huggins, Cynthia Rudin:
A Statistical Learning Theory Framework for Supervised Pattern Discovery. SDM 2014: 506-514 - [i13]Siong Thye Goh, Cynthia Rudin:
Box Drawings for Learning with Imbalanced Data. CoRR abs/1403.3378 (2014) - [i12]Berk Ustun, Cynthia Rudin:
Methods and Models for Interpretable Linear Classification. CoRR abs/1405.4047 (2014) - [i11]Theja Tulabandhula, Cynthia Rudin:
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge. CoRR abs/1405.7764 (2014) - [i10]Theja Tulabandhula, Cynthia Rudin:
Robust Optimization using Machine Learning for Uncertainty Sets. CoRR abs/1407.1097 (2014) - [i9]Fulton Wang, Cynthia Rudin:
Falling Rule Lists. CoRR abs/1411.5899 (2014) - [i8]Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner:
Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports 4(3): 1-27 (2014) - 2013
- [j13]Benjamin Letham, Cynthia Rudin, Katherine A. Heller:
Growing a list. Data Min. Knowl. Discov. 27(3): 372-395 (2013) - [j12]Theja Tulabandhula, Cynthia Rudin:
Machine learning with operational costs. J. Mach. Learn. Res. 14(1): 1989-2028 (2013) - [j11]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The rate of convergence of AdaBoost. J. Mach. Learn. Res. 14(1): 2315-2347 (2013) - [j10]Cynthia Rudin, Benjamin Letham, David Madigan:
Learning theory analysis for association rules and sequential event prediction. J. Mach. Learn. Res. 14(1): 3441-3492 (2013) - [j9]Benjamin Letham, Cynthia Rudin, David Madigan:
Sequential event prediction. Mach. Learn. 93(2-3): 357-380 (2013) - [c22]Seyda Ertekin, Cynthia Rudin, Tyler H. McCormick:
Predicting Power Failures with Reactive Point Processes. AAAI (Late-Breaking Developments) 2013 - [c21]Been Kim, Cynthia Rudin:
Machine Learning for Meeting Analysis. AAAI (Late-Breaking Developments) 2013 - [c20]Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan:
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. AAAI (Late-Breaking Developments) 2013 - [c19]Berk Ustun, Stefano Tracà, Cynthia Rudin:
Supersparse Linear Integer Models for Predictive Scoring Systems. AAAI (Late-Breaking Developments) 2013 - [c18]Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri:
Detecting Patterns of Crime with Series Finder. AAAI (Late-Breaking Developments) 2013 - [c17]Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri:
Learning to Detect Patterns of Crime. ECML/PKDD (3) 2013: 515-530 - [i7]Been Kim, Cynthia Rudin:
Learning About Meetings. CoRR abs/1306.1927 (2013) - [i6]Jonathan H. Huggins, Cynthia Rudin:
Toward a Theory of Pattern Discovery. CoRR abs/1307.0802 (2013) - 2012
- [j8]Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas, Gloria Chou:
How to reverse-engineer quality rankings. Mach. Learn. 88(3): 369-398 (2012) - [j7]Cynthia Rudin, David L. Waltz, Roger Anderson, Albert Boulanger
, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome:
Machine Learning for theNew York City Power Grid. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 328-345 (2012) - [c16]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Selective Sampling of Labelers for Approximating the Crowd. AAAI Fall Symposium: Machine Aggregation of Human Judgment 2012 - [c15]Cynthia Rudin, Theja Tulabandhula:
The Influence of Operational Cost on Estimation. ISAIM 2012 - [c14]Allison Chang, Dimitris Bertsimas, Cynthia Rudin:
An Integer Optimization Approach to Associative Classification. NIPS 2012: 269-277 - [c13]Boyi Xie, Rebecca J. Passonneau, Haimonti Dutta, Jing-Yeu Miaw, Axinia Radeva, Ashish Tomar, Cynthia Rudin:
Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid. SEKE 2012: 100-105 - [c12]Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
Open Problem: Does AdaBoost Always Cycle? COLT 2012: 46.1-46.4 - [i5]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Learning to Predict the Wisdom of Crowds. CoRR abs/1204.3611 (2012) - 2011
- [j6]Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Steve Ierome, Delfina Isaac:
21st-Century Data Miners Meet 19th-Century Electrical Cables. Computer 44(6): 103-105 (2011) - [j5]Seyda Ertekin, Cynthia Rudin:
On Equivalence Relationships Between Classification and Ranking Algorithms. J. Mach. Learn. Res. 12: 2905-2929 (2011) - [c11]Theja Tulabandhula, Cynthia Rudin, Patrick Jaillet:
The Machine Learning and Traveling Repairman Problem. ADT 2011: 262-276 - [c10]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The Rate of Convergence of Adaboost. COLT 2011: 537-558 - [c9]Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan:
Sequential Event Prediction with Association Rules. COLT 2011: 615-634 - [i4]Theja Tulabandhula, Cynthia Rudin, Patrick Jaillet:
Machine Learning and the Traveling Repairman. CoRR abs/1104.5061 (2011) - [i3]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The Rate of Convergence of AdaBoost. CoRR abs/1106.6024 (2011) - [i2]Theja Tulabandhula, Cynthia Rudin:
Machine Learning with Operational Costs. CoRR abs/1112.0698 (2011) - 2010
- [j4]Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac:
A process for predicting manhole events in Manhattan. Mach. Learn. 80(1): 1-31 (2010)
2000 – 2009
- 2009
- [j3]Cynthia Rudin, Robert E. Schapire:
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. J. Mach. Learn. Res. 10: 2193-2232 (2009) - [j2]Cynthia Rudin:
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. J. Mach. Learn. Res. 10: 2233-2271 (2009) - [c8]Rebecca J. Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu:
Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods. CICLing 2009: 86-97 - [c7]Raphael Pelossof, Michael Jones, Ilia Vovsha, Cynthia Rudin:
Online coordinate boosting. ICCV Workshops 2009: 1354-1361 - [c6]Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, Delfina Isaac:
Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task. ICMLA 2009: 719-724 - 2008
- [c5]Ryan Roth, Owen Rambow, Nizar Habash, Mona T. Diab, Cynthia Rudin:
Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. ACL (2) 2008: 117-120 - 2006
- [c4]Cynthia Rudin:
Ranking with a P-Norm Push. COLT 2006: 589-604 - 2005
- [c3]Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire:
Margin-Based Ranking Meets Boosting in the Middle. COLT 2005: 63-78 - [i1]Cynthia Rudin:
Stability Analysis for Regularized Least Squares Regression. CoRR abs/cs/0502016 (2005) - 2004
- [j1]Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. J. Mach. Learn. Res. 5: 1557-1595 (2004) - [c2]Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
Boosting Based on a Smooth Margin. COLT 2004: 502-517 - 2003
- [c1]Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
On the Dynamics of Boosting. NIPS 2003: 1101-1108
Coauthor Index
![](https://dblp.dagstuhl.de/img/cog.dark.24x24.png)
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